✅ FITS IN (W8A8 QUANTIZATION
Llama-4-Maverick-17B-128E (400B MoE) on 8x NVIDIA H100 80GB (640GB) (640GB VRAM)
Full FP16 (826.16 GB) exceeds 640 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 251.93 GB (249.77 GB weights + 1.5 GB KV cache at 8K context), delivering ~201.5 tokens/sec.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
251.93 GB / 640 GB
🟦 Weights (249.77 GB)
🟪 KV Cache (1.5 GB)
🟨 Activation Buffer (0.66 GB)
Total Required VRAM251.93 GB
Available Hardware VRAM640 GB (8x NVIDIA H100 80GB (640GB))
VRAM Headroom / Overflow388.07 GB
Estimated Generation Speed~201.5 tokens/sec
Architecture & AttentionMoE-GQA (48L, 8 KV heads)
🚀 Ready-to-Run Launch Command
ollama run llama4:maverick # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-4-Maverick-17B-128E-Instruct-GGUF -c 8192 -ngl 99 -fa
CLOUD GPU ALTERNATIVE
Need 251.93 GB+ VRAM to run Llama-4-Maverick-17B-128E (400B MoE)?
Recommended cloud setup for 4-bit: Multi-GPU H100 / H200 Node (or 826.16 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
| Provider | Best For | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Bonus / Trial | Action |
|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
| Vast.ai | Cheapest Spot GPU Market | $0.29 / hr | $0.95 / hr | $1.79 / hr | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Check Spot GPUs → |
| Vultr Cloud GPU | Enterprise SLA & Bare Metal | — | $1.79 / hr | $2.69 / hr | $300 Free Credit for New Users | Claim $300 Credit → |
| DigitalOcean GPU Droplets | Managed H100 Droplets | — | — | $2.99 / hr | $200 Free Trial (60 Days) | Get $200 Free → |
All Quantization Formats: Llama-4-Maverick-17B-128E (400B MoE) on 8x NVIDIA H100 80GB (640GB)
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x 8x NVIDIA H100 80GB (640GB) (640GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 824 GB | 1.5 GB | 826.16 GB | No (Over by 186.2GB) | ~7.9 tok/s | 2x 8x NVIDIA H100 80GB (640GB) |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 437.75 GB | 1.5 GB | 439.91 GB | Yes (69%) | ~115 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 437.75 GB | 1.5 GB | 439.91 GB | Yes (69%) | ~115 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| GGUF Q6_K (6-bit) | 6.56 bpw | 337.84 GB | 1.5 GB | 340 GB | Yes (53%) | ~149 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 293.04 GB | 1.5 GB | 295.19 GB | Yes (46%) | ~171.8 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 249.77 GB | 1.5 GB | 251.93 GB | Yes (39%) | ~201.5 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 218.88 GB | 1.5 GB | 221.03 GB | Yes (35%) | ~230 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 201.37 GB | 1.5 GB | 203.52 GB | Yes (32%) | ~250 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
Frequently Asked Questions (Llama-4-Maverick-17B-128E (400B MoE) + 8x NVIDIA H100 80GB (640GB))
Can 8x NVIDIA H100 80GB (640GB) (640GB) run Llama-4-Maverick-17B-128E (400B MoE) locally?
Yes — 8x NVIDIA H100 80GB (640GB) (640GB) can run Llama-4-Maverick-17B-128E (400B MoE) using FP8 (W8A8 / E4M3). Full FP16 (826.16 GB) exceeds 640 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 251.93 GB (249.77 GB weights + 1.5 GB KV cache at 8K context), delivering ~201.5 tokens/sec.
How is KV Cache calculated for Llama-4-Maverick-17B-128E (400B MoE)?
Llama-4-Maverick-17B-128E (400B MoE) uses MoE-GQA with 48 transformer layers, 40 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 1.5 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 0.75 GB.
Compare Llama-4-Maverick-17B-128E (400B MoE) on Other GPUs
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